Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data.

Background: The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods: We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results: The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion: The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications. [ABSTRACT FROM AUTHOR]